Original research
Monitoring fitness, fatigue and running performance during a pre-season training camp in elite football players

https://doi.org/10.1016/j.jsams.2012.12.003Get rights and content

Abstract

Objectives

To examine the usefulness of selected physiological and perceptual measures to monitor fitness, fatigue and running performance during a pre-season, 2-week training camp in eighteen professional Australian Rules Football players (21.9 ± 2.0 years).

Design

Observational.

Methods

Training load, perceived ratings of wellness (e.g. fatigue, sleep quality) and salivary cortisol were collected daily. Submaximal exercise heart rate (HRex) and a vagal-related heart rate variability index (LnSD1) were also collected at the start of each training session. Yo-Yo Intermittent Recovery level 2 test (Yo-YoIR2, assessed pre-, mid- and post-camp, temperate conditions) and high-speed running distance during standardized drills (HSR, >14.4 km h−1, 4 times throughout, outdoor) were used as performance measures.

Results

There were significant (P < 0.001 for all) day-to-day variations in training load (coefficient of variation, CV: 66%), wellness measures (6–18%), HRex (3.3%), LnSD1 (19.0%), but not cortisol (20.0%, P = 0.60). While the overall wellness (+0.06, 90% CL (−0.14; 0.02) AU day−1) did not change substantially throughout the camp, HRex decreased (−0.51 (−0.58; −0.45) % day−1), and cortisol (+0.31 (0.06; 0.57) nmol L−1 day−1), LnSD1 (+0.1 (0.04; 0.06) ms day−1), Yo-YoIR2 performance (+23.7 (20.8; 26.6) m day−1, P < 0.001), and HSR (+4.1 (1.5; 6.6) m day−1, P < 0.001) increased. Day-to-day ΔHRex (r = 0.80, 90% CL (0.75; 0.85)), ΔLnSD1 (0.51 (r = 0.40; 0.62)) and all wellness measures (0.28 (−0.39; −0.17) < r < 0.25 (0.14; 0.36)) were related to Δtraining load. There was however no clear relationship between Δcortisol and Δtraining load. ΔYo-YoIR2 was correlated with ΔHRex (r = 0.88 (0.84; 0.92)), ΔLnSD1 (r = 0.78 (0.67; 0.89)), Δwellness (r = 0.58 (0.41; 0.75), but not Δcortisol. ΔHSR was correlated with ΔHRex (r = −0.27 (−0.48; −0.06)) and Δwellness (r = 0.65 (0.49; 0.81)), but neither with ΔLnSD1 nor Δcortisol.

Conclusions

Training load, HRex and wellness measures are the best simple measures for monitoring training responses to an intensified training camp; cortisol post-exercise and LnSD1 did not show practical efficacy here.

Introduction

One of the main goals of the pre-season training phase in team sports is to develop fitness in preparation for the impending competition season.1 Compared with the in-season, training loads (TL) are generally increased up to 2–4 times during the pre-season period.1 Programming training during the pre-season can be challenging for coaches, since they are required to prescribe TLs that both maximize positive physiological adaptations, while avoiding overtraining and injury. Therefore, the precise control of TL and individual responses to training is essential for maximizing training adaptations.2

Although multiple indices for monitoring both TL and training status have been suggested,2 their invasive (e.g. blood markers3) and/or exhaustive (e.g. (supra)maximal tests4) nature makes their frequent use difficult with elite athletes. Additionally, while TL assessment via heart rate (HR) measures is well accepted in endurance sports, this method is questionable in team sports since the overall TL often comprises of workouts that do not include a significant cardiorespiratory component (e.g. strength/speed training).2 For this reason, the use of the rating of perceived exertion (RPE) based method has emerged as a practical and valid method of estimating TL in team sports.5 With respect to non-invasive and non-exhaustive measures of assessing fitness, wellness (e.g. stress, fatigue), recovery status and physical performance, submaximal exercise HR (HRex) and post-exercise cardiac autonomic activity as inferred from heart rate variability (HRV) measures have recently received increased interest.6 Despite some limitations,7 HRex is considered an index of cardiorespiratory fitness which is strongly correlated with running performance.7, 8 HRV measures have been shown to reflect acute fatigue (i.e. homeostasis perturbation) following exercise,9 and have been used to make inferences about appropriate training periodization.10 The use of salivary hormones such as cortisol, a stress hormone that mediates catabolic activity,4 has also increased in team sports, largely because of its non-invasive nature.11, 12 Finally, psychological monitoring is also purported to be an effective means of assessing players’ responses to training,2, 4, 13 and responds well to subtle TL variations.11 However, despite the possible advantages of the aforementioned variables, it is still unclear how useful these measures are for monitoring changes in wellness, recovery status and in turn, fitness, during an intense training period in elite team sport players. Importantly, the relationship of these variables with physical performance has only been assessed under standardized exercise conditions (i.e. HR-derived measures vs. incremental test,7, 8 10-km run8 or Yo-Yo Intermittent Recovery test14). Whether these measures can also track changes in running performance during less controlled but more sport-specific conditions such as during outdoor ball games is unknown.

The purpose of this study was therefore to (1) document the daily variations of selected physiological and psychometric variables during an intense pre-season training camp in professional football players, and (2) examine their usefulness for monitoring training responses (i.e. fatigue status, fitness, and high-speed running performance during both a Yo-Yo Intermittent Recovery test15 and standardized playing drills).

Section snippets

Methods

Eighteen professional Australian Rules Football (ARF) players (21.9 ± 2.0 years, 189 ± 8 cm and 87.8 ± 9.1 kg) participated in this study, which was approved by the University of Technology, Sydney (UTS) Human Research Ethics Committee. All players provided written informed consent. Prior to inclusion into the study, players were examined by a sports physician and were deemed to be free from illness/injury. The data analyzed in the present study are part of a larger study,16 where half of the team was

Results

Changes in TL, HR-derived measures, wellness and cortisol are shown in Fig. 1. There were significant day-to-day variations in TL (coefficient of variation, CV: 66%, P < 0.001), all wellness measures (6–18%, P < 0.001 for all), HRex (3.3%, P < 0.001), LnSD1 (19.0%, P < 0.001), but not cortisol (20.0%, P = 0.60).

Whilst wellness (0.06, 90% CL (−0.14; 0.02) AU day−1) did not change substantially throughout the camp, HRex decreased (−0.51 (−0.58; −0.45) % day−1), and cortisol (+0.31 (0.06; 0.57) nmol L−1 day−1) and

Discussion

We examined the usefulness of selected physiological and psychometric measures for monitoring fitness, physiological adaptations, wellness and high-intensity running performance during a pre-season camp in professional players. The main results were: (1) Yo-YoIR2 performance and HSR during standardized drills increased substantially throughout the camp; (2) HRex, LnSD1 and all wellness measures, but not cortisol, were sensitive to subtle changes in daily TL; (3) changes in HRex were largely

Conclusion

HRex and all wellness measures, but not cortisol, are highly sensitive to subtle daily changes in TL and are well correlated with positive changes in high-intensity running performance during both Yo-YoIR2 in temperate conditions and standardized drills in the heat. Additionally, changes in HRex are also associated with changes in plasma volume. The present results suggest that RPE-based TL quantification, HRex and wellness measures are useful variables for monitoring positive training

Practical implications

  • Managing daily TL is important, since subtle TL variations modify the physiological and wellness status of highly-trained football players during intense training phases.

  • TL, HRex and wellness measures, but not salivary cortisol, can be used to monitor training-induced changes in recovery status and fatigue, as well as positive changes in both generic and sport-specific running performance. In the context of the present study however, post-exercise HRV measures (i.e. LnSD1) may add little

Acknowledgements

No external financial support was received for this study. We thank S. Livingston and R. Christian for their help in data collection.

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